Link prediction in paper citation network to construct paper correlation graph
Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the user...
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| Published in | EURASIP journal on wireless communications and networking Vol. 2019; no. 1; pp. 1 - 12 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
16.10.2019
Springer Nature B.V SpringerOpen |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance. |
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| AbstractList | Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance. Abstract Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance. |
| ArticleNumber | 233 |
| Author | Kou, Huaizhen Qi, Lianyong Liu, Hanwen Yan, Chao |
| Author_xml | – sequence: 1 givenname: Hanwen surname: Liu fullname: Liu, Hanwen organization: School of Information Science and Engineering, Qufu Normal University – sequence: 2 givenname: Huaizhen surname: Kou fullname: Kou, Huaizhen organization: School of Information Science and Engineering, Qufu Normal University – sequence: 3 givenname: Chao surname: Yan fullname: Yan, Chao organization: School of Information Science and Engineering, Qufu Normal University – sequence: 4 givenname: Lianyong surname: Qi fullname: Qi, Lianyong email: lianyongqi@gmail.com organization: School of Information Science and Engineering, Qufu Normal University |
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| SubjectTerms | Authors’ information Citation analysis Communications Engineering Correlation Engineering Information Systems Applications (incl.Internet) Keywords Link prediction Multi-modal Sensor Data Fusion in Internet of Things Networks Paper citation network Paper correlated graph Recommender systems Signal,Image and Speech Processing Time User requirements User satisfaction |
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| Title | Link prediction in paper citation network to construct paper correlation graph |
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